Connect GitHub to Count
GitHub Analytics with Count
Transform your GitHub repository data into actionable engineering insights with Count's AI-powered github pull request analytics platform. Your development workflow generates massive amounts of data — commits, pull requests, code reviews, deployments, issues, and security alerts — but extracting meaningful patterns from this information manually is nearly impossible.
Traditional approaches fall short: spreadsheet analysis becomes unwieldy with thousands of pull requests and multiple repositories, while GitHub's built-in reporting offers rigid dashboards that can't adapt to your specific questions or combine data across different timeframes and team structures.
AI-Powered GitHub Analysis
Count's AI agent writes custom SQL and Python logic tailored to your exact questions — no templates, no limitations. Ask "Which developers are creating the most review bottlenecks?" or "How does our deployment frequency correlate with bug fix rates?" and Count runs hundreds of queries in seconds to uncover hidden patterns in your development data.
Whether you're tracking Code Review Velocity, analyzing Pull Request Bottleneck Analysis, or measuring Developer Productivity Score, Count handles messy GitHub data automatically — cleaning inconsistencies, normalizing timestamps, and connecting related events across your repositories.
Beyond Basic GitHub Reporting
Count transforms your GitHub data into presentation-ready analysis with transparent methodology you can verify and share. Your team can collaborate on insights, ask follow-up questions, and combine GitHub metrics with data from Jira, Linear, or your database for complete engineering intelligence.
This github reporting tool turns complex development questions into clear, actionable insights — helping you optimize team performance, reduce cycle times, and improve code quality across your entire engineering organization.
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Sign upMetrics & Analyses You Can Run
Code Review Velocity
Measure how quickly pull requests move through the review process from creation to merge in your GitHub repositories.
Deployment Frequency
Track how often your team deploys code to production using GitHub Actions and release data.
Lead Time For Changes
Calculate the time from first commit to production deployment for changes in your GitHub workflow.
Pull Request Approval Rate
Monitor the percentage of pull requests that get approved versus rejected in your GitHub repositories.
Bug Fix Rate
Analyze how quickly your team resolves bug-related issues and pull requests in GitHub.
Commit Frequency
Track developer activity and code contribution patterns through GitHub commit data over time.
Code Coverage Trend
Monitor test coverage changes across your GitHub repositories using CI/CD pipeline data.
Security Alert Resolution Time
Measure how quickly your team addresses security vulnerabilities detected by GitHub's security features.
Developer Productivity Score
Combine multiple GitHub metrics to create a comprehensive view of individual developer performance and output.
Release Velocity
Track the frequency and timing of releases using GitHub tags and release data.
Issue Resolution Time
Analyze how long it takes to close GitHub issues from creation to resolution.
Code Churn Rate
Identify code stability by measuring how frequently files are modified in your GitHub repositories.
Branch Lifecycle Analysis
Track the lifespan of feature branches from creation to merge or deletion in GitHub.
Developer Contribution Patterns
Analyze individual developer work patterns, including commit timing and code contribution distribution from GitHub data.
Pull Request Bottleneck Analysis
Identify where pull requests get stuck in your GitHub workflow and what causes review delays.
Code Quality Trend Analysis
Monitor code quality metrics over time using GitHub's code scanning and review feedback data.
Repository Health Score
Assess overall repository health using GitHub metrics like activity, documentation, and maintenance indicators.
Team Collaboration Index
Measure team collaboration effectiveness through GitHub pull request reviews, comments, and cross-developer interactions.
Feature Development Cycle Time
Track the complete lifecycle of feature development from branch creation to merge in GitHub.
Technical Debt Accumulation
Monitor technical debt growth using GitHub code complexity metrics and TODO comments tracking.
Devops Pipeline Efficiency
Analyze GitHub Actions workflow performance and CI/CD pipeline success rates and timing.
Security Vulnerability Trends
Track security vulnerability discovery and resolution patterns using GitHub's security advisory data.
Open Source Contribution Analysis
Analyze external contributions to your GitHub repositories including pull requests from outside collaborators.
Sprint Velocity Tracking
Measure development velocity by tracking GitHub milestone and project completion rates over time.
Code Review Quality Score
Evaluate the thoroughness and effectiveness of code reviews using GitHub pull request comment and approval data.
Discussion Engagement Rate
Track community engagement levels in GitHub Discussions and issue comment activity.